Performance evaluation of propensity score methods for estimating average treatment effects with multi-level treatments*

The propensity score (PS) method is widely used to estimate the average treatment effect (TE) in observational studies. However, it is generally confined to the binary treatment assignment. In an extension to the settings of a multi-level treatment, Imbens proposed a generalized propensity score which is the conditional probability of receiving a particular level of the treatment given pre-treatment variables. The average TE can then be estimated by conditioning solely on the generalized PS under the assumption of weak unconfoundedness. In the present work, we adopted this approach and conducted extensive simulations to evaluate the performance of several methods using the generalized PS, including subclassification, matching, inverse probability of treatment weighting (IPTW), and covariate adjustment. Compared with other methods, IPTW had the preferred overall performance. We then applied these methods to a retrospective cohort study of 228,876 pregnant women. The impact of the exposure to different types of the antidepressant medications (no exposure, selective serotonin reuptake inhibitor (SSRI) only, non-SSRI only, and both) during pregnancy on several important infant outcomes (birth weight, gestation age, preterm labor, and respiratory distress) were assessed.倾向得分（propensity score, PS）方法在观察性研究中被广泛用于估计平均治疗效应（treatment effect, TE），但该方法通常局限于二分类治疗分配场景。针对多水平治疗的研究设置，因本斯（Imbens）提出了广义倾向得分，即给定前处理变量时接受某一特定治疗水平的条件概率。在弱无混杂假设下，仅需基于广义倾向得分进行条件分析，即可估计平均治疗效应。本研究采用该方法，开展了大规模模拟实验，以评估基于广义倾向得分的多种方法的性能，包括亚分类、匹配、治疗加权逆概率（inverse probability of treatment weighting, IPTW）以及协变量调整。结果显示，与其他方法相比，治疗加权逆概率法拥有更优的综合性能。随后，我们将这些方法应用于一项纳入228876名孕妇的回顾性队列研究，评估了妊娠期间暴露于不同类型抗抑郁药物（无暴露、仅选择性5-羟色胺再摄取抑制剂（selective serotonin reuptake inhibitor, SSRI）、仅非SSRI类药物、同时暴露于两类药物）对多项重要婴儿结局（出生体重、胎龄、早产、呼吸窘迫）的影响。